Specify GBTM method

Group-based trajectory modeling through fixed-effects modeling.

lcMethodLcmmGBTM(
  fixed,
  mixture = ~1,
  classmb = ~1,
  time = getOption("latrend.time"),
  id = getOption("latrend.id"),
  nClusters = 2,
  init = "default",
  ...
)

Arguments

fixed

The fixed effects formula.

mixture

The mixture-specific effects formula. See lcmm::hlme for details.

classmb

The cluster membership formula for the multinomial logistic model. See lcmm::hlme for details.

time

The name of the time variable.

id

The name of the trajectory identifier variable. This replaces the subject argument of lcmm::hlme.

nClusters

The number of clusters to fit. This replaces the ng argument of lcmm::hlme.

init

Alternative for the B argument of lcmm::hlme, for initializing the hlme fitting procedure. This is only applicable for nClusters > 1. Options:

• "lme.random" (default): random initialization through a standard linear mixed model. Assigns a fitted standard linear mixed model enclosed in a call to random() to the B argument.

• "lme", fits a standard linear mixed model and passes this to the B argument.

• "gridsearch", a gridsearch is used with initialization from "lme.random", following the approach used by lcmm::gridsearch. To use this initalization, specify arguments gridsearch.maxiter (max number of iterations during search), gridsearch.rep (number of fits during search), and gridsearch.parallel (whether to enable parallel computation).

• NULL or "default", the default lcmm::hlme input for B is used.

The argument is ignored if the B argument is specified, or nClusters = 1.

...

Arguments passed to lcmm::hlme. The following arguments are ignored: data, fixed, random, mixture, subject, classmb, returndata, ng, verbose, subset.

References

Proust-Lima C, Philipps V, Liquet B (2017). “Estimation of Extended Mixed Models Using Latent Classes and Latent Processes: The R Package lcmm.” Journal of Statistical Software, 78(2), 1–56. doi:10.18637/jss.v078.i02 .

Proust-Lima C, Philipps V, Diakite A, Liquet B (2019). lcmm: Extended Mixed Models Using Latent Classes and Latent Processes. R package version: 1.8.1, https://cran.r-project.org/package=lcmm.

See also

Other lcMethod implementations: getArgumentDefaults(), getArgumentExclusions(), lcMethod-class, lcMethodAkmedoids, lcMethodCrimCV, lcMethodDtwclust, lcMethodFeature, lcMethodFunFEM, lcMethodFunction, lcMethodGCKM, lcMethodKML, lcMethodLMKM, lcMethodLcmmGMM, lcMethodMclustLLPA, lcMethodMixAK_GLMM, lcMethodMixtoolsGMM, lcMethodMixtoolsNPRM, lcMethodRandom, lcMethodStratify

Examples

data(latrendData)
if (rlang::is_installed("lcmm")) {
  method <- lcMethodLcmmGBTM(
    fixed = Y ~ Time,
    mixture = ~ 1,
   id = "Id",
   time = "Time",
   nClusters = 3
  )
  gbtm <- latrend(method, data = latrendData)
  summary(gbtm)

  method <- lcMethodLcmmGBTM(
    fixed = Y ~ Time,
    mixture = ~ Time,
    id = "Id",
    time = "Time",
    nClusters = 3
  )
}